正在工作的python代码停止工作

Question

一切正常

cv_results = model_selection.cross_val_score(model, X_train, Y_train, cv=kfold, score=scoring)

    import pandas
    from pandas.plotting import scatter_matrix
    import matplotlib.pyplot as plt
    from sklearn import model_selection
    from sklearn.metrics import classification_report
    from sklearn.metrics import confusion_matrix
    from sklearn.metrics import accuracy_score
    from sklearn.linear_model import LogisticRegression
    from sklearn.tree import DecisionTreeClassifier
    from sklearn.neighbors import KNeighborsClassifier
    from sklearn.discriminant_analysis import LinearDiscriminantAnalysis
    from sklearn.naive_bayes import GaussianNB
    from sklearn.svm import SVC

    # Load dataset (contains floats and one boolean)
    url = "\\File\\Path.csv"
    names = ['Headers', 'Here', 'A', 'B', 'C', 'D', 'E', 'F', 'G', 'H', 'I', 'J', 'T/F']
    dataset = pandas.read_csv(url, names=names)

    # Split-out validation dataset
    array = dataset.values
    X = array[:,0:12]
    Y = array[:,12]
    validation_size = 0.10
    seed = 7
    X_train, X_validation, Y_train, Y_validation = model_selection.train_test_split(X, Y, test_size=validation_size, random_state=seed)

    # Test options and evaluation metric
    seed = 7
    scoring = 'accuracy'

    # Spot check algorithms
    models = []
    models.append(('LR', LogisticRegression()))
    models.append(('LDA', LinearDiscriminantAnalysis()))
    models.append(('KNN', KNeighborsClassifier()))
    models.append(('CART', DecisionTreeClassifier()))
    models.append(('NB', GaussianNB()))
    models.append(('SVM', SVC()))

    # evaluate each model in turn
    results = []
    names = []
    for name, model in models:
        kfold = model_selection.KFold(n_splits=10, random_state=seed)
        cv_results = model_selection.cross_val_score(model, X_train, Y_train, cv=kfold, scoring=scoring)
        results.append(cv_results)
        names.append(name)
        msg = "%s: %f (%f)" % (name, cv_results.mean(), cv_results.std())
        print(msg)

    # Compare Algorithms
    fig = plt.figure()
    fig.suptitle('Algorithm Comparison')
    ax = fig.add_subplot(111)
    plt.boxplot(results)
    ax.set_xticklabels(names)
    plt.show()

当它到达这部分时它会停止

cv_results = model_selection.cross_val_score(model, X_train, Y_train, cv=kfold, score=scoring)

    Warning (from warnings module):
      File "C:\Python\Python37-32\lib\site-packages\sklearn\linear_model\logistic.py", line 433
        FutureWarning)
    FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.

    Warning (from warnings module):
      File "C:\Python\Python37-32\lib\site-packages\sklearn\model_selection\_validation.py", line 542
        FutureWarning)
    FutureWarning: From version 0.22, errors during fit will result in a cross validation score of NaN by default. Use error_score='raise' if you want an exception raised or error_score=np.nan to adopt the behavior from version 0.22.
    Traceback (most recent call last):
      File "/test.py", line 46, in <module>
        cv_results = model_selection.cross_val_score(model, X_train, Y_train, cv=kfold, scoring=scoring)
      File "C:\Python\Python37-32\lib\site-packages\sklearn\model_selection\_validation.py", line 402, in cross_val_score
        error_score=error_score)
      File "C:\Python\Python37-32\lib\site-packages\sklearn\model_selection\_validation.py", line 240, in cross_validate
        for train, test in cv.split(X, y, groups))
      File "C:\Python\Python37-32\lib\site-packages\sklearn\externals\joblib\parallel.py", line 917, in __call__
        if self.dispatch_one_batch(iterator):
      File "C:\Python\Python37-32\lib\site-packages\sklearn\externals\joblib\parallel.py", line 759, in dispatch_one_batch
        self._dispatch(tasks)
      File "C:\Python\Python37-32\lib\site-packages\sklearn\externals\joblib\parallel.py", line 716, in _dispatch
        job = self._backend.apply_async(batch, callback=cb)
      File "C:\Python\Python37-32\lib\site-packages\sklearn\externals\joblib\_parallel_backends.py", line 182, in apply_async
        result = ImmediateResult(func)
      File "C:\Python\Python37-32\lib\site-packages\sklearn\externals\joblib\_parallel_backends.py", line 549, in __init__
        self.results = batch()
      File "C:\Python\Python37-32\lib\site-packages\sklearn\externals\joblib\parallel.py", line 225, in __call__
        for func, args, kwargs in self.items]
      File "C:\Python\Python37-32\lib\site-packages\sklearn\externals\joblib\parallel.py", line 225, in <listcomp>
        for func, args, kwargs in self.items]
      File "C:\Python\Python37-32\lib\site-packages\sklearn\model_selection\_validation.py", line 528, in _fit_and_score
        estimator.fit(X_train, y_train, **fit_params)
      File "C:\Python\Python37-32\lib\site-packages\sklearn\linear_model\logistic.py", line 1289, in fit
        check_classification_targets(y)
      File "C:\Python\Python37-32\lib\site-packages\sklearn\utils\multiclass.py", line 171, in check_classification_targets
        raise ValueError("Unknown label type: %r" % y_type)
    ValueError: Unknown label type: 'unknown'

它运行良好，直到它到达

cv_results = model_selection.cross_val_score(model, X_train, Y_train, cv=kfold, score=scoring)

Answer 1

在创建 y_train 和 y_validator 变量后添加：

Y_train = Y_train.astype('float')
Y_validator = Y_validation.astype('float')

当您读取 y 变量时，它被存储为一个对象，因此 sklearn 不知道如何处理它（因此错误 ValueError("Unknown label type: %r" % y_type). 将 Y_train 和 Y_test 更改为 float 或 int 类型应该可以修复错误